{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4acdcdfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adc6e84f",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       "      <th>男50米跑</th>\n",
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       "      <th>男跳远</th>\n",
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       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
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       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
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       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
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       "4       68  3538      74  171  54.700001  18.709999  \n",
       "..     ...   ...     ...  ...        ...        ...  \n",
       "472      0  4647     100  176  69.500000  22.440001  \n",
       "473     50  7042     100  177  76.000000  24.260000  \n",
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       "475     76  5688     100  172  51.700001  17.480000  \n",
       "476      0     0       0    0   0.000000   0.000000  \n",
       "\n",
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       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0       85  3775     100  163.0  51.299999  19.309999  \n",
       "1       66  3683     100  163.0  66.599998  25.070000  \n",
       "2       76  3331     100  157.0  60.000000  24.340000  \n",
       "3       85  3701     100  160.0  50.700001  19.799999  \n",
       "4       70  3592     100  167.0  63.900002  22.910000  \n",
       "..     ...   ...     ...    ...        ...        ...  \n",
       "588     78  2255      70  158.0  49.000000  19.629999  \n",
       "589     72  2937      85  161.0  55.700001  21.490000  \n",
       "590     72  2592      76  165.0  48.599998  17.850000  \n",
       "591     78  1829      60  154.0  43.599998  18.379999  \n",
       "592     85  2962      85  162.0  55.299999  21.070000  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "man = pd.read_excel('./体测分数_男生.xls')\n",
    "woman  = pd.read_excel('./体测分数_女生.xls')\n",
    "display(man,woman)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6e4c32d",
   "metadata": {},
   "source": [
    "### 对男1000米跑、男引体进行等宽分箱操作，分成3份，并使用饼图绘制百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba564f27",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>男1000米跑等级</th>\n",
       "      <th>男引体等级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>良好</td>\n",
       "      <td>良好</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>100</td>\n",
       "      <td>176</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>22.440001</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>7042</td>\n",
       "      <td>100</td>\n",
       "      <td>177</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>24.260000</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
       "      <td>良好</td>\n",
       "      <td>良好</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>5688</td>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>51.700001</td>\n",
       "      <td>17.480000</td>\n",
       "      <td>优秀</td>\n",
       "      <td>良好</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>不及格</td>\n",
       "      <td>不及格</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI 男1000米跑等级 男引体等级  \n",
       "0        0  2785      62  170  72.599998  25.120001        优秀   不及格  \n",
       "1       60  3133      68  174  52.700001  17.410000        优秀   不及格  \n",
       "2        0  3901      80  169  46.500000  16.280001        优秀   不及格  \n",
       "3        0  4946     100  183  79.699997  23.799999        优秀   不及格  \n",
       "4       68  3538      74  171  54.700001  18.709999        良好    良好  \n",
       "..     ...   ...     ...  ...        ...        ...       ...   ...  \n",
       "472      0  4647     100  176  69.500000  22.440001        优秀   不及格  \n",
       "473     50  7042     100  177  76.000000  24.260000        优秀   不及格  \n",
       "474     85  5755     100  181  65.000000  19.840000        良好    良好  \n",
       "475     76  5688     100  172  51.700001  17.480000        优秀    良好  \n",
       "476      0     0       0    0   0.000000   0.000000       不及格   不及格  \n",
       "\n",
       "[477 rows x 19 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "man['男1000米跑等级'] = pd.cut(man.男1000米跑,#分箱数据\n",
    "      bins = 3,#分成三份\n",
    "      right = False,#\n",
    "      labels = ['不及格','良好','优秀'])\n",
    "man['男引体等级'] = pd.cut(man.男引体,#分箱数据\n",
    "               bins = 3,\n",
    "               right = False,\n",
    "               labels = ['不及格','良好','优秀'])\n",
    "man"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "882b4c90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '男引体等级百分比')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_num = (man['男1000米跑等级'][man['男1000米跑等级'] == '优秀'].count()/man['男1000米跑等级'].count(),\n",
    "           man['男1000米跑等级'][man['男1000米跑等级'] == '良好'].count()/man['男1000米跑等级'].count(),\n",
    "           man['男1000米跑等级'][man['男1000米跑等级'] == '不及格'].count()/man['男1000米跑等级'].count())#计算不同等级人数占比\n",
    "labels_run = ['优秀','良好','不及格']\n",
    "up_num = (man['男引体等级'][man['男引体等级'] == '优秀'].count()/man['男引体等级'].count(),\n",
    "           man['男引体等级'][man['男引体等级'] == '良好'].count()/man['男引体等级'].count(),\n",
    "           man['男引体等级'][man['男引体等级'] == '不及格'].count()/man['男引体等级'].count())#计算不同等级人数占比\n",
    "labels_up = ['优秀','良好','不及格']\n",
    "\n",
    "plt.figure(figsize = (9,9))\n",
    "run_rate = plt.subplot(121)\n",
    "\n",
    "\n",
    "run_rate = plt.pie(run_num,\n",
    "                 labels = labels_run,\n",
    "                 autopct = '%0.2f%%',#显示百分比\n",
    "                   shadow = True,\n",
    "                   explode = [0,0,0.35],\n",
    "                   textprops = {'family':'Kaiti','fontsize':18})\n",
    "plt.title('男1000米跑等级百分比',fontfamily = 'Kaiti',fontsize = 18)\n",
    "up_rate = plt.subplot(122)\n",
    "up_rate = plt.pie(up_num,\n",
    "                 labels = labels_up,\n",
    "                 autopct = '%0.2f%%',#显示百分比\n",
    "                   shadow = True,\n",
    "                  explode = [0.35,0,0],\n",
    "                   textprops = {'family':'Kaiti','fontsize':18})\n",
    "plt.title('男引体等级百分比',fontfamily = 'Kaiti',fontsize = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f45c4907",
   "metadata": {},
   "source": [
    "### 对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2fe6bf57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>女800米跑等级</th>\n",
       "      <th>女跳远等级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78</td>\n",
       "      <td>9.60</td>\n",
       "      <td>68</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>2255</td>\n",
       "      <td>70</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>19.629999</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>21.490000</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>2592</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI 女800米跑等级 女跳远等级  \n",
       "0       85  3775     100  163.0  51.299999  19.309999        A     A  \n",
       "1       66  3683     100  163.0  66.599998  25.070000        C     B  \n",
       "2       76  3331     100  157.0  60.000000  24.340000        A     B  \n",
       "3       85  3701     100  160.0  50.700001  19.799999        A     A  \n",
       "4       70  3592     100  167.0  63.900002  22.910000        A     B  \n",
       "..     ...   ...     ...    ...        ...        ...      ...   ...  \n",
       "588     78  2255      70  158.0  49.000000  19.629999        A     B  \n",
       "589     72  2937      85  161.0  55.700001  21.490000        A     B  \n",
       "590     72  2592      76  165.0  48.599998  17.850000        A     B  \n",
       "591     78  1829      60  154.0  43.599998  18.379999        B     B  \n",
       "592     85  2962      85  162.0  55.299999  21.070000        B     A  \n",
       "\n",
       "[593 rows x 19 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woman['女800米跑等级'] = pd.cut(woman.女800米跑分数,#分箱数据\n",
    "      bins = 4,#分成四份\n",
    "      right = False,\n",
    "      labels = ['D','C','B','A'])\n",
    "woman['女跳远等级'] = pd.cut(woman.女跳远分数,#分箱数据\n",
    "               bins = 4,\n",
    "               right = False,\n",
    "               labels = ['D','C','B','A'])\n",
    "woman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "8d1f4d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "x1 = woman['女800米跑分数']\n",
    "x2 = woman['女跳远分数']\n",
    "\n",
    "woman_run_rate = plt.subplot(121)\n",
    "woman_run_rate = plt.hist(x1,bins =4,color='orange',density=False)\n",
    "woman_run_rate = plt.title(label = '女800米跑分数',fontfamily = 'Kaiti',fontsize =18,color='white',backgroundcolor='#c5b783') \n",
    "\n",
    "woman_jump_rate = plt.subplot(122)\n",
    "woman_jump_rate = plt.hist(x2,bins = 4,color='blue',density=False)\n",
    "woman_jump_rate = plt.title(label = '女跳远分数',fontfamily = 'Kaiti',fontsize =18,color='white',backgroundcolor='#c5b783') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba737b42",
   "metadata": {},
   "source": [
    "### 使用嵌套饼图对比男女生体重指数进行比例统计，分为正常、低体重、超重、肥胖(男女生体重指数参考如下)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "ac9e15e8",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>男1000米跑等级</th>\n",
       "      <th>男引体等级</th>\n",
       "      <th>BMI等级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "      <td>低体重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>良好</td>\n",
       "      <td>良好</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>100</td>\n",
       "      <td>176</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>22.440001</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>7042</td>\n",
       "      <td>100</td>\n",
       "      <td>177</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>24.260000</td>\n",
       "      <td>优秀</td>\n",
       "      <td>不及格</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
       "      <td>良好</td>\n",
       "      <td>良好</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>5688</td>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>51.700001</td>\n",
       "      <td>17.480000</td>\n",
       "      <td>优秀</td>\n",
       "      <td>良好</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>不及格</td>\n",
       "      <td>不及格</td>\n",
       "      <td>低体重</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI 男1000米跑等级 男引体等级 BMI等级  \n",
       "0        0  2785      62  170  72.599998  25.120001        优秀   不及格    超重  \n",
       "1       60  3133      68  174  52.700001  17.410000        优秀   不及格    正常  \n",
       "2        0  3901      80  169  46.500000  16.280001        优秀   不及格   低体重  \n",
       "3        0  4946     100  183  79.699997  23.799999        优秀   不及格    超重  \n",
       "4       68  3538      74  171  54.700001  18.709999        良好    良好    正常  \n",
       "..     ...   ...     ...  ...        ...        ...       ...   ...   ...  \n",
       "472      0  4647     100  176  69.500000  22.440001        优秀   不及格    正常  \n",
       "473     50  7042     100  177  76.000000  24.260000        优秀   不及格    超重  \n",
       "474     85  5755     100  181  65.000000  19.840000        良好    良好    正常  \n",
       "475     76  5688     100  172  51.700001  17.480000        优秀    良好    正常  \n",
       "476      0     0       0    0   0.000000   0.000000       不及格   不及格   低体重  \n",
       "\n",
       "[477 rows x 20 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def bmi_man(x) :#定义一个对男生bmi进行分级的函数\n",
    "    if x  <= 16.4:\n",
    "        return '低体重'\n",
    "    elif x <= 23.2:\n",
    "        return '正常'\n",
    "    elif x <= 26.3:\n",
    "        return '超重'\n",
    "    else :\n",
    "        return '肥胖'\n",
    "man['BMI等级'] = man['BMI'].map(bmi_man)\n",
    "display(man)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "779d95af",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>女800米跑等级</th>\n",
       "      <th>女跳远等级</th>\n",
       "      <th>BMI等级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>超重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78</td>\n",
       "      <td>9.60</td>\n",
       "      <td>68</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>2255</td>\n",
       "      <td>70</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>19.629999</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>21.490000</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>2592</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI 女800米跑等级 女跳远等级 BMI等级  \n",
       "0       85  3775     100  163.0  51.299999  19.309999        A     A    正常  \n",
       "1       66  3683     100  163.0  66.599998  25.070000        C     B    超重  \n",
       "2       76  3331     100  157.0  60.000000  24.340000        A     B    超重  \n",
       "3       85  3701     100  160.0  50.700001  19.799999        A     A    正常  \n",
       "4       70  3592     100  167.0  63.900002  22.910000        A     B    超重  \n",
       "..     ...   ...     ...    ...        ...        ...      ...   ...   ...  \n",
       "588     78  2255      70  158.0  49.000000  19.629999        A     B    正常  \n",
       "589     72  2937      85  161.0  55.700001  21.490000        A     B    正常  \n",
       "590     72  2592      76  165.0  48.599998  17.850000        A     B    正常  \n",
       "591     78  1829      60  154.0  43.599998  18.379999        B     B    正常  \n",
       "592     85  2962      85  162.0  55.299999  21.070000        B     A    正常  \n",
       "\n",
       "[593 rows x 20 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def bmi_woman(x) :\n",
    "    if x  <= 16.4:\n",
    "        return '低体重'\n",
    "    elif x <= 22.7:\n",
    "        return '正常'\n",
    "    elif x <= 25.2:\n",
    "        return '超重'\n",
    "    else :\n",
    "        return '肥胖'\n",
    "woman['BMI等级'] = woman['BMI'].map(bmi_woman)\n",
    "display(woman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "00886774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
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   ],
   "source": [
    "p1 = (man['BMI等级'][man['BMI等级'] == '低体重'].count()/man['BMI等级'].count(),\n",
    "      man['BMI等级'][man['BMI等级'] == '正常'].count()/man['BMI等级'].count(),\n",
    "      man['BMI等级'][man['BMI等级'] == '超重'].count()/man['BMI等级'].count(),\n",
    "      man['BMI等级'][man['BMI等级'] == '肥胖'].count()/man['BMI等级'].count()) # 外圈\n",
    "\n",
    "p2 = (woman['BMI等级'][woman['BMI等级'] == '低体重'].count()/woman['BMI等级'].count(),\n",
    "      woman['BMI等级'][woman['BMI等级'] == '正常'].count()/woman['BMI等级'].count(),\n",
    "      woman['BMI等级'][woman['BMI等级'] == '超重'].count()/woman['BMI等级'].count(),\n",
    "      woman['BMI等级'][woman['BMI等级'] == '肥胖'].count()/woman['BMI等级'].count()) # 外圈\n",
    "plt.figure(figsize=(9,9))\n",
    "cs = ['#4682B4', '#DC143C', '#228B22', '#FF8C00']\n",
    "plt.pie(p1,radius=1,\n",
    "        autopct='%0.2f%%',\n",
    "        colors = cs,\n",
    "        pctdistance=0.85,\n",
    "        labels = ['低体重','正常','超重','肥胖'],\n",
    "        wedgeprops={'linewidth':5,# 间隔的宽度\n",
    "                    'width':0.3, # 饼图的宽度\n",
    "                    'edgecolor':'white'},# 间隔的颜色\n",
    "        textprops={'family':'Kaiti','fontsize':18})\n",
    "\n",
    "_ = plt.pie(p2,\n",
    "        radius=0.7,\n",
    "        autopct='%0.2f%%',\n",
    "        colors = cs,\n",
    "        pctdistance=0.55,\n",
    "        wedgeprops={'linewidth':5,# 间隔的宽度\n",
    "                    'width':0.7, # 饼图的宽度\n",
    "                    'edgecolor':'white'})# 间隔的颜色\n",
    "_ = plt.title('           男女生体重比例          ',\n",
    "              fontsize = 30,\n",
    "              fontfamily = 'Kaiti',\n",
    "              weight = 'bold',\n",
    "              color = 'white', \n",
    "              backgroundcolor = '#c5b783',\n",
    "              pad = 30)\n",
    "_ = plt.legend(['低体重','正常','超重','肥胖'],prop = 'Kaiti',fontsize = 18)"
   ]
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